{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PC4WDB6WYIQF6OKYWKCRBCDH2D","short_pith_number":"pith:PC4WDB6W","schema_version":"1.0","canonical_sha256":"78b96187d6c2205f3958b285108867d0dc1ba2a7b424b48f77ae67437252ac23","source":{"kind":"arxiv","id":"2305.12296","version":2},"attestation_state":"computed","paper":{"title":"PhotoMat: A Material Generator Learned from Single Flash Photos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Kalyan Sunkavalli, Milo\\v{s} Ha\\v{s}an, Nima Khademi Kalantari, Paul Guerrero, Valentin Deschaintre, Xilong Zhou, Yannick Hold-Geoffroy","submitted_at":"2023-05-20T22:27:41Z","abstract_excerpt":"Authoring high-quality digital materials is key to realism in 3D rendering. Previous generative models for materials have been trained exclusively on synthetic data; such data is limited in availability and has a visual gap to real materials. We circumvent this limitation by proposing PhotoMat: the first material generator trained exclusively on real photos of material samples captured using a cell phone camera with flash. Supervision on individual material maps is not available in this setting. Instead, we train a generator for a neural material representation that is rendered with a learned "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2305.12296","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-20T22:27:41Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"b984d2c414c10f0a04b2d308943423422decddd9fc5728bfb680d0cf1f1badda","abstract_canon_sha256":"b0ac4ee9ed67fa85f38da24d51bd7822235a067cd34129940c65224be6a1c5e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:51.139851Z","signature_b64":"RfC6/bgbKSOtLn0rotPsXk86s6BAjbueVuCt6K2y34mfKTiIeY5oUC4Neg/vvD7pSq8Mk6rWSNu3XJLR2mkFBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78b96187d6c2205f3958b285108867d0dc1ba2a7b424b48f77ae67437252ac23","last_reissued_at":"2026-07-05T06:12:51.139436Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:51.139436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PhotoMat: A Material Generator Learned from Single Flash Photos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Kalyan Sunkavalli, Milo\\v{s} Ha\\v{s}an, Nima Khademi Kalantari, Paul Guerrero, Valentin Deschaintre, Xilong Zhou, Yannick Hold-Geoffroy","submitted_at":"2023-05-20T22:27:41Z","abstract_excerpt":"Authoring high-quality digital materials is key to realism in 3D rendering. Previous generative models for materials have been trained exclusively on synthetic data; such data is limited in availability and has a visual gap to real materials. We circumvent this limitation by proposing PhotoMat: the first material generator trained exclusively on real photos of material samples captured using a cell phone camera with flash. Supervision on individual material maps is not available in this setting. Instead, we train a generator for a neural material representation that is rendered with a learned "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12296","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.12296/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2305.12296","created_at":"2026-07-05T06:12:51.139491+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.12296v2","created_at":"2026-07-05T06:12:51.139491+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12296","created_at":"2026-07-05T06:12:51.139491+00:00"},{"alias_kind":"pith_short_12","alias_value":"PC4WDB6WYIQF","created_at":"2026-07-05T06:12:51.139491+00:00"},{"alias_kind":"pith_short_16","alias_value":"PC4WDB6WYIQF6OKY","created_at":"2026-07-05T06:12:51.139491+00:00"},{"alias_kind":"pith_short_8","alias_value":"PC4WDB6W","created_at":"2026-07-05T06:12:51.139491+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D","json":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D.json","graph_json":"https://pith.science/api/pith-number/PC4WDB6WYIQF6OKYWKCRBCDH2D/graph.json","events_json":"https://pith.science/api/pith-number/PC4WDB6WYIQF6OKYWKCRBCDH2D/events.json","paper":"https://pith.science/paper/PC4WDB6W"},"agent_actions":{"view_html":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D","download_json":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D.json","view_paper":"https://pith.science/paper/PC4WDB6W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.12296&json=true","fetch_graph":"https://pith.science/api/pith-number/PC4WDB6WYIQF6OKYWKCRBCDH2D/graph.json","fetch_events":"https://pith.science/api/pith-number/PC4WDB6WYIQF6OKYWKCRBCDH2D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D/action/storage_attestation","attest_author":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D/action/author_attestation","sign_citation":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D/action/citation_signature","submit_replication":"https://pith.science/pith/PC4WDB6WYIQF6OKYWKCRBCDH2D/action/replication_record"}},"created_at":"2026-07-05T06:12:51.139491+00:00","updated_at":"2026-07-05T06:12:51.139491+00:00"}